Developments in the field of deep learning have facilitated progress in a plethora of tasks related to computer vision. To implement these computer vision advancements on devices with relatively low battery budgets, such as smartphones, recent work has focused on designing custom hardware for inference in deep neural networks. Embedded image processing systems for computer vision typically involve an entire imaging pipeline, from detecting photons to obtaining a task result. Existing image processing system pipelines are designed to produce high-quality images for human consumption. A typical image system pipeline consists of an image sensor and an image signal processor chip, both of which are hardwired to produce high-resolution, low-noise, color corrected photographs. As advancements in computer vision hardware reduces the energy cost of inference, the cost to capture and process images consumes a larger share of total system power.